CNN-Based Distracted Driver Recognition: Enhancing Accuracy Through Optimized Training Techniques

Abstract. Distracted driving is a significant contributor to vehicle accidents worldwide, underscoring the need for an effective recognition model that can identify risky driver behaviors in real-time and provide timely alerts. This study proposes a Convolutional Neural Network (CNN)-based model designed to accurately detect distracted drivers. To optimize the model's performance, this paper implemented several strategic enhancements. During the preprocessing phase, 90% of the dataset was allocated for training, ensuring a comprehensive learning process, while the remaining 10% was used for validation to assess the model's accuracy. The Adam optimizer was chosen for its ability to dynamically adjust the learning rate, facilitating faster convergence to an optimal solution. Additionally, the Cross-Entropy loss function was employed to amplify errors during training, driving the model to correct inaccuracies more effectively. The model was trained over 25 epochs, resulting in an accuracy rate of nearly 80%. This level of performance demonstrates the models viability for real-world applications, where it can play a critical role in reducing accidents caused by distracted driving. Future research may focus on further refining the model by exploring advanced loss functions, optimizers, and CNN architectures, as well as incorporating more sophisticated data preprocessing techniques.